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首页> 外文会议>Tenth Color Imaging Conference: Color Science and Engineering Systems, Technologies, Applications Nov 12-15, 2002 Scottsdale, Arizona, USA >Multi-Scale Image Sharpening with Background Noise Suppression
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Multi-Scale Image Sharpening with Background Noise Suppression

机译：具有背景噪声抑制功能的多尺度图像锐化

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摘要

The color image has a various edge profile. In the conventional single kernel filter has the drawbacks of exhausting background noises or insensitivity to the dull edges. In the previous paper, we proposed a new image sharpening method adaptive to the edge profiles. In this paper, we present its advanced model, which has both sharpening and smoothing functions. In addition, the paper assesses the edge sharpness factors by introducing the indices, such as ES (Edge Sharpness), FS (Spatial Frequency Sharpness), and Nf (Flat Area Noise). The improved model makes the flat area noises intentionally smoothed with preserving the enhanced edges. The sharpening filter is applied only to the luminance Y image to keep the gray balance. After pre-scanning the Y image with sharp GD filter, the edge map is generated by classifying the edge types into hard, medium, soft, and flat zones. The multiple GD operators with different deviations, σ_1, σ_2, and σ_3 are selectively applied to the corresponding edge zones of Y image by looking up the edge map. Here the smoothing filters are applied only to the flat zones to reduce the background noises. In simple, the normal Gaussian filter is used as a noise smoother. In comparison with conventional method, the proposed model worked excellent to sharpen the different edge slopes naturally together with dramatically reducing the background noises.
机译：彩色图像具有各种边缘轮廓。在常规的单核滤波器中，具有耗尽背景噪声或对钝边缘不敏感的缺点。在先前的论文中，我们提出了一种新的图像锐化方法，以适应边缘轮廓。在本文中，我们介绍了它的高级模型，该模型同时具有锐化和平滑功能。此外，本文通过介绍诸如ES（边缘清晰度），FS（空间频率清晰度）和Nf（平坦区域噪声）等指标来评估边缘清晰度因素。改进的模型通过保留增强的边缘，有意地平滑了平面噪声。锐化滤镜仅应用于亮度Y图像以保持灰度平衡。在使用清晰的GD滤镜对Y图像进行预扫描后，通过将边缘类型分为硬，中，软和平坦区域来生成边缘图。通过查找边缘图，将具有不同偏差σ_1，σ_2和σ_3的多个GD算子选择性地应用于Y图像的相应边缘区域。此处，平滑滤波器仅应用于平坦区域，以减少背景噪声。简而言之，将普通高斯滤波器用作噪声平滑器。与传统方法相比，所提出的模型在自然地锐化不同的边缘斜率以及显着降低背景噪声方面表现出色。

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